Generative AI Projects — Comprehensive Roadmap

Author: Moh Rafik
Duration: Month 3 (Weeks 9–12)
Learning Time: 6–7 hrs/day
Goal: Understand, implement, and experiment with modern Generative AI techniques — VAEs, GANs, Diffusion Models, and Transformers.

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📘 Overview

This repository is part of the AI Learning Roadmap (3-Month Intensive) that includes:

1. Machine Learning Basics
2. Deep Learning Foundations
3. Generative AI Projects ← (You are here)

This repository focuses on:

• Building a conceptual and practical understanding of Generative AI
• Implementing and training core models like Autoencoders, VAEs, GANs, and Diffusion Models
• Exploring modern architectures like Transformers and LLMs
• Building mini-projects for text and image generation

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📂 Repository Structure

generative-ai-projects/
│
├── 01_autoencoders/
│   ├── basic_autoencoder.ipynb
│   ├── variational_autoencoder.ipynb
│
├── 02_gans/
│   ├── vanilla_gan.ipynb
│   ├── dcgan_faces.ipynb
│   ├── cycle_gan_intro.ipynb
│
├── 03_diffusion_models/
│   ├── diffusion_intro.ipynb
│   ├── stable_diffusion_walkthrough.ipynb
│
├── 04_transformers_llms/
│   ├── gpt2_finetuning.ipynb
│   ├── text_generation_huggingface.ipynb
│   ├── prompt_engineering_examples.ipynb
│
├── projects/
│   ├── text_to_image_pipeline.ipynb
│   ├── custom_gan_dataset.ipynb
│
├── assets/
│   ├── images/
│   └── figures/
│
└── README.md

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📖 Learning Path (4-Week Plan)

Week	Focus	Topics
9	Autoencoders & VAEs	Representation learning, latent space exploration
10	GANs	Adversarial training, DCGANs, Conditional GANs
11	Diffusion Models	Denoising processes, Stable Diffusion basics
12	LLMs & Prompt Engineering	GPT, fine-tuning, text-to-image pipelines

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🧮 Theoretical Summaries

1. Autoencoders (AEs)

• Concept: Learn compressed representations (encodings) of data.
• Structure: Encoder → Bottleneck → Decoder.
• Loss Function: Reconstruction loss (MSE).
• Applications: Noise removal, dimensionality reduction.

2. Variational Autoencoders (VAEs)

• Extension of AEs: Introduces probabilistic latent variables.
• Loss: Combination of Reconstruction + KL Divergence.
• Formula:
  ( L = E_{q(z|x)}[log \ p(x|z)] - KL[q(z|x) || p(z)] )

3. Generative Adversarial Networks (GANs)

• Components: Generator (G) and Discriminator (D) in a minimax game.
• Objective:
  ( \min_G \max_D V(D, G) = E_{x \sim p_{data}}[log D(x)] + E_{z \sim p_z}[log(1 - D(G(z)))] )
• Common Variants: DCGAN, CycleGAN, StyleGAN.

4. Diffusion Models

• Idea: Learn to reverse a diffusion (noise-adding) process.
• Famous Models: DDPM, Stable Diffusion.
• Applications: Image generation, denoising, inpainting.

5. Transformers & LLMs

• Mechanism: Self-Attention, Multi-Head Attention, Positional Encoding.
• Popular Models: GPT, BERT, T5.
• Applications: Text generation, summarization, translation.

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💻 Implementation Summary

Libraries

• PyTorch, TensorFlow, Keras – model implementations
• Hugging Face Transformers – fine-tuning and inference
• Diffusers – Stable Diffusion toolkit
• Matplotlib, NumPy, Torchvision – visualization and datasets

Key Implementations

1. Build Autoencoder & VAE from scratch.
2. Train DCGAN on MNIST and CelebA datasets.
3. Experiment with Diffusion Models (using Hugging Face).
4. Fine-tune GPT-2 on custom text data.
5. Build a Text-to-Image Pipeline using open models.

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🚀 Projects

🧱 1. Variational Autoencoder on MNIST

Goal: Compress and reconstruct handwritten digits.
Concepts: KL divergence, latent space sampling.
Deliverables: Visualizations of latent space clusters.

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🧠 2. DCGAN for Face Generation

Goal: Generate realistic human faces using adversarial training.
Concepts: Generator vs. Discriminator training loop.
Dataset: CelebA.
Deliverables: Saved generated images during training.

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🌫️ 3. Diffusion Model Experiment

Goal: Learn denoising diffusion probabilistic model (DDPM).
Concepts: Forward and reverse diffusion processes.
Deliverables: Generate new images from Gaussian noise.

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🗣️ 4. GPT-2 Fine-Tuning on Custom Dataset

Goal: Fine-tune GPT-2 for domain-specific text generation.
Concepts: Tokenization, causal language modeling.
Dataset: Custom text corpus.
Deliverables: Generate coherent domain-specific text samples.

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🖼️ 5. Text-to-Image Pipeline

Goal: Combine transformer and diffusion components.
Concepts: Prompt-based generation, CLIP guidance.
Deliverables: Generate image outputs from textual prompts.

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📊 Results Summary

Model	Dataset	Output	Notes
VAE	MNIST	Reconstructed digits	Clear latent structure
DCGAN	CelebA	Generated faces	Improved over epochs
DDPM	CIFAR-10	Generated images	Stable results
GPT-2	Custom	Domain text	Fine-tuned successfully
Text-to-Image	Prompts	Realistic images	Used CLIP + Diffusers

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🧠 Key Takeaways

• Understood probabilistic generative models.
• Implemented GANs and learned adversarial optimization.
• Trained diffusion-based models for image synthesis.
• Fine-tuned and deployed transformer-based language models.
• Built creative multimodal applications (Text → Image).

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🧩 Next Step

🎯 Apply knowledge to real-world domains: biomedical imaging, text synthesis, or digital art generation.

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🧰 Tools & Environment

• Python 3.9+
• Jupyter Notebook / VS Code
• Libraries: torch, torchvision, transformers, diffusers, matplotlib

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📚 References

• MIT 6.S192 — Deep Generative Models
• David Foster — Generative Deep Learning
• Hugging Face Docs — https://huggingface.co/docs
• DDPM Paper — Ho et al., 2020
• GAN Paper — Goodfellow et al., 2014

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✅ Progress Checklist

Task	Status
Implement Autoencoder and VAE	☐
Train DCGAN	☐
Experiment with Diffusion Models	☐
Fine-tune GPT-2	☐
Build Text-to-Image pipeline	☐
Complete all 5 projects	☐
Final documentation & Git push	☐

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⭐ Pro Tip:
Document every experiment visually. Include GIFs of training progression, sample generations, and model performance graphs for an impressive GitHub portfolio.

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📌 Maintained by Moh Rafik
💬 For queries or collaborations: [RAFIKIITBHU@GMAIL.COM or LinkedIn]